lightning/CHANGELOG.md

Changelog

All notable changes to this project will be documented in this file.

The format is based on Keep a Changelog.

[unreleased] - YYYY-MM-DD

Added

• Added parity test between a vanilla MNIST model and lightning model (#1284)
• Added Reinforcement Learning - Deep Q-network (DQN) lightning example (#1232)
• Added support for hierarchical dict (#1152)
• Added TrainsLogger class (#1122)
• Added type hints to pytorch_lightning.core (#946)
• Added support for IterableDataset in validation and testing (#1104)
• Added support for non-primitive types in hparams for TensorboardLogger (#1130)
• Added a check that stops the training when loss or weights contain NaN or inf values. (#1097)
• Updated references to self.forward() to instead use the __call__ interface. (#1211)
• Added support for IterableDataset when val_check_interval=1.0 (default), this will trigger validation at the end of each epoch. (#1283)
• Added summary method to Profilers. (#1259)
• Added informative errors if user defined dataloader has zero length (#1280)
• Added testing for python 3.8 (#915)
• Added a training_epoch_end method which is the mirror of validation_epoch_end. (#1357)
• Added model configuration checking (#1199)
• Added support for optimizer frequencies through LightningModule.configure_optimizers() (#1269)
• Added option to run without an optimizer by returning None from configure_optimizers. (#1279)

Changed

• Changed progress_bar_refresh_rate trainer flag to disable progress bar when set to 0. (#1108)
• Enhanced load_from_checkpoint to also forward params to the model (#1307)
• Updated references to self.forward() to instead use the __call__ interface. (#1211)
• Changed default behaviour of configure_optimizers to use no optimizer rather than Adam. (#1279)
• Allow to upload models on W&B (#1339)
• On DP and DDP2 unsqueeze is automated now (#1319)
• Did not always create a DataLoader during reinstantiation, but the same type as before (if subclass of DataLoader) (#1346)
• Did not interfere with a default sampler (#1318)
• Remove default Adam optimizer (#1317)
• Give warnings for unimplemented required lightning methods (#1317)
• Enhanced load_from_checkpoint to also forward params to the model (#1307)
• Made evaluate method private >> Trainer._evaluate(...). (#1260)

Deprecated

• Deprecated Trainer argument print_nan_grads (#1097)
• Deprecated Trainer argument show_progress_bar (#1108)

Removed

• Removed duplicated module pytorch_lightning.utilities.arg_parse for loading CLI arguments (#1167)
• Removed wandb logger's finalize method (#1193)
• Dropped torchvision dependency in tests and added own MNIST dataset class instead (#986)

Fixed

• Trainer.add_argparse_args classmethod fixed. Now it adds a type for the arguments (#1147).
• Fixed bug related to type cheking of ReduceLROnPlateau lr schedulers(#1114)
• Fixed a bug to ensure lightning checkpoints to be backward compatible (#1132)
• Fixed a bug that created an extra dataloader with active reload_dataloaders_every_epoch (#1181
• Fixed all warnings and errors in the docs build process (#1191)
• Fixed an issue where val_percent_check=0 would not disable validation (#1251)
• Fixed average of incomplete TensorRunningMean (#1309)
• Fixed WandbLogger.watch with wandb.init() (#1311)
• Fixed an issue with early stopping that would prevent it from monitoring training metrics when validation is disabled / not implemented (#1235).
• Fixed a bug that would cause trainer.test() to run on the validation set when overloading validation_epoch_end and test_end (#1353).
• Fixed WandbLogger.watch (#1311)

[0.7.1] - 2020-03-07

Fixed

• Fixes print issues and data_loader (#1080)

[0.7.0] - 2020-03-06

Added

• Added automatic sampler setup. Depending on DDP or TPU, lightning configures the sampler correctly (user needs to do nothing) (#926)
• Added reload_dataloaders_every_epoch=False flag for trainer. Some users require reloading data every epoch (#926)
• Added progress_bar_refresh_rate=50 flag for trainer. Throttle refresh rate on notebooks (#926)
• Updated governance docs
• Added a check to ensure that the metric used for early stopping exists before training commences (#542)
• Added optimizer_idx argument to backward hook (#733)
• Added entity argument to WandbLogger to be passed to wandb.init (#783)
• Added a tool for profiling training runs (#782)
• Improved flexibility for naming of TensorBoard logs, can now set version to a str to just save to that directory, and use name='' to prevent experiment-name directory (#804)
• Added option to specify step key when logging metrics (#808)
• Added train_dataloader, val_dataloader and test_dataloader arguments to Trainer.fit(), for alternative data parsing (#759)
• Added Tensor Processing Unit (TPU) support (#868)
• Added semantic segmentation example (#751,#876, #881)
• Split callbacks in multiple files (#849)
• Support for user defined callbacks (#889 and #950)
• Added support for multiple loggers to be passed to Trainer as an iterable (e.g. list, tuple, etc.) (#903)
• Added support for step-based learning rate scheduling (#941)
• Added support for logging hparams as dict (#1029)
• Checkpoint and early stopping now work without val. step (#1041)
• Support graceful training cleanup after Keyboard Interrupt (#856, #1019)
• Added type hints for function arguments (#912, )
• Added default argparser for Trainer (#952, #1023)
• Added TPU gradient clipping (#963)
• Added max/min number of steps in Trainer (#728)

Changed

• Improved NeptuneLogger by adding close_after_fit argument to allow logging after training(#908)
• Changed default TQDM to use tqdm.auto for prettier outputs in IPython notebooks (#752)
• Changed pytorch_lightning.logging to pytorch_lightning.loggers (#767)
• Moved the default tqdm_dict definition from Trainer to LightningModule, so it can be overridden by the user (#749)
• Moved functionality of LightningModule.load_from_metrics into LightningModule.load_from_checkpoint (#995)
• Changed Checkpoint path parameter from filepath to dirpath (#1016)
• Freezed models hparams as Namespace property (#1029)
• Dropped logging config in package init (#1015)
• Renames model steps (#1051)
  • training_end >> training_epoch_end
  • validation_end >> validation_epoch_end
  • test_end >> test_epoch_end
• Refactor dataloading, supports infinite dataloader (#955)
• Create single file in TensorBoardLogger (#777)

Deprecated

• Deprecated pytorch_lightning.logging (#767)
• Deprecated LightningModule.load_from_metrics in favour of LightningModule.load_from_checkpoint (#995, #1079)
• Deprecated @data_loader decorator (#926)
• Deprecated model steps training_end, validation_end and test_end (#1051, #1056)

Removed

• Removed dependency on pandas (#736)
• Removed dependency on torchvision (#797)
• Removed dependency on scikit-learn (#801)

Fixed

• Fixed a bug where early stopping on_end_epoch would be called inconsistently when check_val_every_n_epoch == 0 (#743)
• Fixed a bug where the model checkpointer didn't write to the same directory as the logger (#771)
• Fixed a bug where the TensorBoardLogger class would create an additional empty log file during fitting (#777)
• Fixed a bug where global_step was advanced incorrectly when using accumulate_grad_batches > 1 (#832)
• Fixed a bug when calling self.logger.experiment with multiple loggers (#1009)
• Fixed a bug when calling logger.append_tags on a NeptuneLogger with a single tag (#1009)
• Fixed sending back data from .spawn by saving and loading the trained model in/out of the process (#1017
• Fixed port collision on DDP (#1010)
• Fixed/tested pass overrides (#918)
• Fixed comet logger to log after train (#892)
• Remove deprecated args to learning rate step function (#890)

[0.6.0] - 2020-01-21

Added

• Added support for resuming from a specific checkpoint via resume_from_checkpoint argument (#516)
• Added support for ReduceLROnPlateau scheduler (#320)
• Added support for Apex mode O2 in conjunction with Data Parallel (#493)
• Added option (save_top_k) to save the top k models in the ModelCheckpoint class (#128)
• Added on_train_start and on_train_end hooks to ModelHooks (#598)
• Added TensorBoardLogger (#607)
• Added support for weight summary of model with multiple inputs (#543)
• Added map_location argument to load_from_metrics and load_from_checkpoint (#625)
• Added option to disable validation by setting val_percent_check=0 (#649)
• Added NeptuneLogger class (#648)
• Added WandbLogger class (#627)

Changed

• Changed the default progress bar to print to stdout instead of stderr (#531)
• Renamed step_idx to step, epoch_idx to epoch, max_num_epochs to max_epochs and min_num_epochs to min_epochs (#589)
• Renamed total_batch_nb to total_batches, nb_val_batches to num_val_batches, nb_training_batches to num_training_batches, max_nb_epochs to max_epochs, min_nb_epochs to min_epochs, nb_test_batches to num_test_batches, and nb_val_batches to num_val_batches (#567)
• Changed gradient logging to use parameter names instead of indexes (#660)
• Changed the default logger to TensorBoardLogger (#609)
• Changed the directory for tensorboard logging to be the same as model checkpointing (#706)

Deprecated

• Deprecated max_nb_epochs and min_nb_epochs (#567)
• Deprecated the on_sanity_check_start hook in ModelHooks (#598)

Removed

• Removed the save_best_only argument from ModelCheckpoint, use save_top_k=1 instead (#128)

Fixed

• Fixed a bug which ocurred when using Adagrad with cuda (#554)
• Fixed a bug where training would be on the GPU despite setting gpus=0 or gpus=[] (#561)
• Fixed an error with print_nan_gradients when some parameters do not require gradient (#579)
• Fixed a bug where the progress bar would show an incorrect number of total steps during the validation sanity check when using multiple validation data loaders (#597)
• Fixed support for PyTorch 1.1.0 (#552)
• Fixed an issue with early stopping when using a val_check_interval < 1.0 in Trainer (#492)
• Fixed bugs relating to the CometLogger object that would cause it to not work properly (#481)
• Fixed a bug that would occur when returning -1 from on_batch_start following an early exit or when the batch was None (#509)
• Fixed a potential race condition with several processes trying to create checkpoint directories (#530)
• Fixed a bug where batch 'segments' would remain on the GPU when using truncated_bptt > 1 (#532)
• Fixed a bug when using IterableDataset (#547)
• Fixed a bug where .item was called on non-tensor objects (#602)
• Fixed a bug where Trainer.train would crash on an uninitialized variable if the trainer was run after resuming from a checkpoint that was already at max_epochs (#608)
• Fixed a bug where early stopping would begin two epochs early (#617)
• Fixed a bug where num_training_batches and num_test_batches would sometimes be rounded down to zero (#649)
• Fixed a bug where an additional batch would be processed when manually setting num_training_batches (#653)
• Fixed a bug when batches did not have a .copy method (#701)
• Fixed a bug when using log_gpu_memory=True in Python 3.6 (#715)
• Fixed a bug where checkpoint writing could exit before completion, giving incomplete checkpoints (#689)
• Fixed a bug where on_train_end was not called when ealy stopping (#723)

[0.5.3] - 2019-11-06

Added

• Added option to disable default logger, checkpointer, and early stopping by passing logger=False, checkpoint_callback=False and early_stop_callback=False respectively
• Added CometLogger for use with Comet.ml
• Added val_check_interval argument to Trainer allowing validition to be performed at every given number of batches
• Added functionality to save and load hyperparameters using the standard checkpoint mechanism
• Added call to torch.cuda.empty_cache before training starts
• Added option for user to override the call t backward
• Added support for truncated backprop through time via the truncated_bptt_steps argument in Trainer
• Added option to operate on all outputs from training_step in DDP2
• Added a hook for modifying DDP init
• Added a hook for modifying Apex

Changed

• Changed experiment version to be padded with zeros (e.g. /dir/version_9 becomes /dir/version_0009)
• Changed callback metrics to include any metrics given in logs or progress bar
• Changed the default for save_best_only in ModelCheckpoint to True
• Added tng_data_loader for backwards compatibility
• Renamed MLFlowLogger.client to MLFlowLogger.experiment for consistency
• Moved global_step increment to happen after the batch has been processed
• Changed weights restore to first attempt HPC weights before restoring normally, preventing both weights being restored and running out of memory
• Changed progress bar functionality to add multiple progress bars for train/val/test
• Changed calls to print to use logging instead

Deprecated

• Deprecated tng_dataloader

Fixed

• Fixed an issue where the number of batches was off by one during training
• Fixed a bug that occured when setting a ckeckpoint callback and early_stop_callback=False
• Fixed an error when importing CometLogger
• Fixed a bug where the gpus argument had some unexpected behaviour
• Fixed a bug where the computed total number of batches was sometimes incorrect
• Fixed a bug where the progress bar would sometimes not show the total number of batches in test mode
• Fixed a bug when using the log_gpu_memory='min_max' option in Trainer
• Fixed a bug where checkpointing would sometimes erase the current directory

[0.5.2] - 2019-10-10

Added

• Added weights_summary argument to Trainer to be set to full (full summary), top (just top level modules) or other
• Added tags argument to MLFlowLogger

Changed

• Changed default for amp_level to O1

Removed

• Removed the print_weights_summary argument from Trainer

Fixed

• Fixed a bug where logs were not written properly
• Fixed a bug where logger.finalize wasn't called after training is complete
• Fixed callback metric errors in DDP
• Fixed a bug where TestTubeLogger didn't log to the correct directory

[0.5.1] - 2019-10-05

Added

• Added the LightningLoggerBase class for experiment loggers
• Added MLFlowLogger for logging with mlflow
• Added TestTubeLogger for logging with test_tube
• Added a different implementation of DDP (distributed_backed='ddp2') where every node has one model using all GPUs
• Added support for optimisers which require a closure (e.g. LBFGS)
• Added automatic MASTER_PORT defualt for DDP when not set manually
• Added new GPU memory logging options 'min_max' (log only the min/max utilization) and 'all' (log all the GPU memory)

Changed

• Changed schedulers to always be called with the current epoch
• Changed test_tube to an optional dependency
• Changed data loaders to internally use a getter instead of a python property
• Disabled auto GPU loading when restoring weights to prevent out of memory errors
• Changed logging, early stopping and checkpointing to occur by default

Fixed

• Fixed a bug with samplers that do not specify set_epoch
• Fixed a bug when using the MLFlowLogger with unsupported data types, this will now raise a warning
• Fixed a bug where gradient norms were alwasy zero using track_grad_norm
• Fixed a bug which causes a crash when logging memory

[0.5.0] - 2019-09-26

Changed

• Changed data_batch argument to batch throughout
• Changed batch_i argument to batch_idx throughout
• Changed tng_dataloader method to train_dataloader
• Changed on_tng_metrics method to on_training_metrics
• Changed gradient_clip argument to gradient_clip_val
• Changed add_log_row_interval to row_log_interval

Fixed

• Fixed a bug with tensorboard logging in multi-gpu setup

[0.4.9] - 2019-09-16

Added

• Added the flag log_gpu_memory to Trainer to deactivate logging of GPU memory utilization
• Added SLURM resubmit functionality (port from test-tube)
• Added optional weight_save_path to trainer to remove the need for a checkpoint_callback when using cluster training
• Added option to use single gpu per node with DistributedDataParallel

Changed

• Changed functionality of validation_end and test_end with multiple dataloaders to be given all of the dataloaders at once rather than in seperate calls
• Changed print_nan_grads to only print the parameter value and gradients when they contain NaN
• Changed gpu API to take integers as well (e.g. gpus=2 instead of gpus=[0, 1])
• All models now loaded on to CPU to avoid device and out of memory issues in PyTorch

Fixed

• Fixed a bug where data types that implement .to but not .cuda would not be properly moved onto the GPU
• Fixed a bug where data would not be re-shuffled every epoch when using a DistributedSampler

[0.4.8] - 2019-08-31

Added

• Added test_step and test_end methods, used when Trainer.test is called
• Added GradientAccumulationScheduler callback which can be used to schedule changes to the number of accumulation batches
• Added option to skip the validation sanity check by setting nb_sanity_val_steps = 0

Fixed

• Fixed a bug when setting nb_sanity_val_steps = 0

[0.4.7] - 2019-08-24

Changed

• Changed the default val_check_interval to 1.0
• Changed defaults for nb_val_batches, nb_tng_batches and nb_test_batches to 0

Fixed

• Fixed a bug where the full validation set as used despite setting val_percent_check
• Fixed a bug where an Exception was thrown when using a data set containing a single batch
• Fixed a bug where an Exception was thrown if no val_dataloader was given
• Fixed a bug where tuples were not properly transfered to the GPU
• Fixed a bug where data of a non standard type was not properly handled by the trainer
• Fixed a bug when loading data as a tuple
• Fixed a bug where AttributeError could be suppressed by the Trainer

[0.4.6] - 2019-08-15

Added

• Added support for data to be given as a dict or list with a single gpu
• Added support for configure_optimizers to return a single optimizer, two list (optimizers and schedulers), or a single list

Fixed

• Fixed a bug where returning just an optimizer list (i.e. without schedulers) from configure_optimizers would throw an Exception

[0.4.5] - 2019-08-13

Added

• Added optimizer_step method that can be overridden to change the standard optimizer behaviour

[0.4.4] - 2019-08-12

Added

• Added supoort for multiple validation dataloaders
• Added support for latest test-tube logger (optimised for torch==1.2.0)

Changed

• validation_step and val_dataloader are now optional
• lr_scheduler is now activated after epoch

Fixed

• Fixed a bug where a warning would show when using lr_scheduler in torch>1.1.0
• Fixed a bug where an Exception would be thrown if using torch.DistributedDataParallel without using a DistributedSampler, this now throws a Warning instead

[0.4.3] - 2019-08-10

Fixed

• Fixed a bug where accumulate gradients would scale the loss incorrectly

[0.4.2] - 2019-08-08

Changed

• Changed install requirement to torch==1.2.0

[0.4.1] - 2019-08-08

Changed

• Changed install requirement to torch==1.1.0

[0.4.0] - 2019-08-08

Added

• Added 16-bit support for a single GPU
• Added support for training continuation (preserves epoch, global step etc.)

Changed

• Changed training_step and validation_step, outputs will no longer be automatically reduced

Removed

• Removed need for Experiment object in Trainer

Fixed

• Fixed issues with reducing outputs from generative models (such as images and text)

[0.3.6] - 2019-07-25

Added

• Added a decorator to do lazy data loading internally

Fixed

• Fixed a bug where Experiment object was not process safe, potentially causing logs to be overwritten

[0.3.5] - 2019-MM-DD

[0.3.4] - 2019-MM-DD

[0.3.3] - 2019-MM-DD

[0.3.2] - 2019-MM-DD

[0.3.1] - 2019-MM-DD

[0.2.x] - YYYY-MM-DD

[0.1.x] - YYYY-MM-DD